Research Article
Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients
Algorithm 2
Reduct generation algorithm.
Input: information table with discretized real valued attribute. | Output: reduct sets | 1: for each condition
attributes do | 2: Compute the
correlation factor between and the
decisions attributes | 3: if the correlation
factor > 0 then | 4: Set as relevant attributes. | 5: end if | 6: end for | 7: Divide the set of relevant attribute into a different variable sets. | 8: for each variable sets do | 9: Compute the dependency degree and
compute the classification quality | 10: Let the set with high classification accuracy and high dependency as an initial | reduct set. | 11: end for | 12: for each attribute in the reduct set do | 13: Calculate the degree of dependencies between the decisions attribute and that | attribute. | 14: Merge the attributes produced in previous step with the rest of conditional | attributes | 15: Calculate the discrimination factors for each combination to find the highest | discrimination factors | 16: Add the highest discrimination factors combination to the final reduct set. | 17: end for | 18: repeat | statements 12 | 19: until all attributes in initial reduct set is processed |
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